export telespeech ctc models to sherpa-onnx (#968)

scripts/tele-speech/.gitignore

@@ -0,0 +1 @@
+*.json

scripts/tele-speech/README.md

@@ -0,0 +1,13 @@
+# Introduction
+
+This folder contains scripts about adding metadata to
+onnx models from
+https://hf-mirror.com/lovemefan/telespeech/tree/main
+
+Please see
+
+  - https://github.com/Tele-AI/TeleSpeech-ASR
+  - https://github.com/lovemefan/telespeech-asr-python
+  - [TeleSpeech模型社区许可协议.pdf](https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/TeleSpeech%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
+
+for more details.

scripts/tele-speech/add-metadata.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+
+import json
+from typing import Dict
+
+import onnx
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = value
+
+    onnx.save(model, filename)
+
+
+def main():
+    with open("./vocab.json", "r", encoding="utf-8") as f:
+        tokens = json.load(f)
+
+    vocab_size = len(tokens)
+    with open("tokens.txt", "w", encoding="utf-8") as f:
+        for token, idx in tokens.items():
+            if idx == 0:
+                f.write("<blk> 0\n")
+            else:
+                f.write(f"{token} {idx}\n")
+
+    filename = "model.onnx"
+    meta_data = {
+        "model_type": "telespeech_ctc",
+        "version": "1",
+        "model_author": "Tele-AI",
+        "comment": "See also https://github.com/lovemefan/telespeech-asr-python",
+        "license": "https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/TeleSpeech%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf",
+        "url": "https://github.com/Tele-AI/TeleSpeech-ASR",
+    }
+
+    add_meta_data(filename, meta_data)
+
+    filename_int8 = f"model.int8.onnx"
+    quantize_dynamic(
+        model_input=filename,
+        model_output=filename_int8,
+        op_types_to_quantize=["MatMul"],
+        weight_type=QuantType.QInt8,
+    )
+
+    #  filename_uint8 = f"model.uint8.onnx"
+    #  quantize_dynamic(
+    #      model_input=filename,
+    #      model_output=filename_uint8,
+    #      op_types_to_quantize=["MatMul"],
+    #      weight_type=QuantType.QUInt8,
+    #  )
+
+
+if __name__ == "__main__":
+    main()

scripts/tele-speech/run.sh

@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+
+curl -SL -O https://hf-mirror.com/lovemefan/telespeech/resolve/main/model_export.onnx
+
+mv model_export.onnx model.onnx
+
+curl -SL -O https://hf-mirror.com/lovemefan/telespeech/resolve/main/vocab.json
+
+curl -SL -O https://github.com/csukuangfj/models/releases/download/a/TeleSpeech.pdf
+curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09/resolve/main/test_wavs/3-sichuan.wav
+curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09/resolve/main/test_wavs/4-tianjin.wav
+curl -SL -O https://hf-mirror.com/csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09/resolve/main/test_wavs/5-henan.wav
+
+ls -lh
+
+./add-metadata.py
+
+dst=sherpa-onnx-telespeech-ctc-zh-2024-06-04
+mkdir $dst
+mkdir $dst/test_wavs
+cp -v model.onnx $dst/
+cp -v tokens.txt $dst
+cp -v *.wav $dst/test_wavs
+cp -v *.pdf $dst
+cp -v README.md $dst
+cp -v *.py $dst
+
+ls -lh $dst
+
+tar cvjfv ${dst}.tar.bz2 $dst
+
+dst=sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04
+mkdir $dst
+mkdir $dst/test_wavs
+cp -v model.int8.onnx $dst/
+cp -v tokens.txt $dst
+cp -v *.wav $dst/test_wavs
+cp -v *.pdf $dst
+cp -v README.md $dst
+cp -v *.py $dst
+
+ls -lh $dst
+
+tar cvjfv ${dst}.tar.bz2 $dst
+
+cp -v *.tar.bz2 ../..
+
+ls -lh ../../

scripts/tele-speech/test.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+from typing import Tuple
+
+import kaldi_native_fbank as knf
+import numpy as np
+import onnxruntime as ort
+import soundfile as sf
+
+"""
+NodeArg(name='feats', type='tensor(float)', shape=[1, 'T', 40])
+-----
+NodeArg(name='logits', type='tensor(float)', shape=['Addlogits_dim_0', 1, 7535])
+"""
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        self.show()
+
+    def show(self):
+        for i in self.model.get_inputs():
+            print(i)
+
+        print("-----")
+
+        for i in self.model.get_outputs():
+            print(i)
+
+    def __call__(self, x):
+        """
+        Args:
+          x: a float32 tensor of shape (N, T, C)
+        """
+        logits = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x,
+            },
+        )[0]
+
+        return logits
+
+
+def load_audio(filename: str) -> Tuple[np.ndarray, int]:
+    data, sample_rate = sf.read(
+        filename,
+        always_2d=True,
+        dtype="float32",
+    )
+    data = data[:, 0]  # use only the first channel
+    samples = np.ascontiguousarray(data)
+    return samples, sample_rate
+
+
+def get_features(test_wav_filename):
+    samples, sample_rate = load_audio(test_wav_filename)
+
+    if sample_rate != 16000:
+        import librosa
+
+        samples = librosa.resample(samples, orig_sr=sample_rate, target_sr=16000)
+        sample_rate = 16000
+
+    samples *= 372768
+
+    opts = knf.MfccOptions()
+    # See https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/mfcc_hires.conf
+    opts.frame_opts.dither = 0
+
+    opts.num_ceps = 40
+    opts.use_energy = False
+
+    opts.mel_opts.num_bins = 40
+    opts.mel_opts.low_freq = 40
+    opts.mel_opts.high_freq = -200
+
+    mfcc = knf.OnlineMfcc(opts)
+    mfcc.accept_waveform(16000, samples)
+    frames = []
+    for i in range(mfcc.num_frames_ready):
+        frames.append(mfcc.get_frame(i))
+
+    frames = np.stack(frames, axis=0)
+    return frames
+
+
+def cmvn(features):
+    # See https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/conf/train_d2v2_ark_conformer.yaml#L70
+    # https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/wenet/dataset/dataset.py#L184
+    # https://github.com/Tele-AI/TeleSpeech-ASR/blob/master/wenet_representation/wenet/dataset/processor.py#L278
+    mean = features.mean(axis=0, keepdims=True)
+    std = features.std(axis=0, keepdims=True)
+    return (features - mean) / (std + 1e-5)
+
+
+def main():
+    # Please download the test data from
+    # https://hf-mirror.com/csukuangfj/sherpa-onnx-paraformer-zh-small-2024-03-09/tree/main/test_wavs
+    test_wav_filename = "./3-sichuan.wav"
+    test_wav_filename = "./4-tianjin.wav"
+    test_wav_filename = "./5-henan.wav"
+
+    features = get_features(test_wav_filename)
+
+    features = cmvn(features)
+
+    features = np.expand_dims(features, axis=0)  # (T, C) -> (N, T, C)
+
+    model_filename = "./model.int8.onnx"
+    model = OnnxModel(model_filename)
+    logits = model(features)
+    logits = logits.squeeze(axis=1)  # remove batch axis
+    ids = logits.argmax(axis=-1)
+
+    id2token = dict()
+    with open("./tokens.txt", encoding="utf-8") as f:
+        for line in f:
+            t, idx = line.split()
+            id2token[int(idx)] = t
+
+    tokens = []
+
+    blank = 0
+    prev = -1
+
+    for k in ids:
+        if k != blank and k != prev:
+            tokens.append(k)
+        prev = k
+
+    tokens = [id2token[i] for i in tokens]
+    text = "".join(tokens)
+    print(text)
+
+
+if __name__ == "__main__":
+    main()